System and method for facilitating analysis of game simulation of spectator sports leagues

ABSTRACT

Embodiments described herein facilitate an analysis of a game simulation of a series of sporting events that occur over a duration of time. A plurality of parameters may be associated with individual players in a series of sporting events. An input may be detected from a user of the game simulation. The input may indicate a preference of the user for how one or more of the plurality of parameters are to be valued in relation to one another. An overall value for one or more participants in the game simulation may be indicated, where the overall value corresponds to a value of the plurality of parameters and to the input.

TECHNICAL FIELD

The disclosed embodiments relate generally to the field of gamesimulations of spectator sports. In particular, the disclosedembodiments relate to a system and method for facilitating gamesimulation of a spectator sport.

BACKGROUND

Spectator sports, such as professional or collegiate football, baseball,basketball, and hockey, rely on fan participation to generate revenueand enthusiasm. Over the years, fan participation and enthusiasm hasdriven spectator sports to become highly lucrative. As a result,numerous secondary businesses and events have been fostered and createdbased on the ever increasing popularity of the spectator sport.

Fantasy leagues, in particular, are an increasingly popular andsophisticated derivative of spectator sports. Currently, fantasy leaguesfor football, baseball, basketball and hockey are readily available. Theemergence of the Internet and online environment has in particularfostered the growth behind the fantasy leagues.

In general, a fantasy league simulates, or at least corresponds, toevents of a particular sports league. Fantasy football leagues, forexample, have members that form fantasy teams. Each fantasy team mayhave designated positions corresponding to what an actual sports teamhas. For example, a fantasy football team may have all the offensive,defensive and special team positions of an actual professional footballteam. The members of a fantasy league go through a selection processwhere each makes a selection of a particular player for his team. Amember of a fantasy football league can fill the positions of hisfantasy team by assigning a particular position or role on his team to anamed football player. The team that the named player plays on in theactual sports league is not relevant in the fantasy team. But thestatistics the named player acquires in the course of a season areconverted into fantasy numbers. The member's fantasy team is then rankedagainst other fantasy teams by an aggregate or total value reflectingthe fantasy numbers of all positions or slots in the fantasy team, whereeach such fantasy number is derived from the performance of acorresponding player in the actual sports league. The fantasy numberthat is determined for a player is usually statistical in nature,reflecting both positive statistics (scoring a touchdown, throwing acompleted pass, running yardage) and negative statistics (fumble,interception, incomplete pass) of the player's performance in theseason.

Fantasy numbers may be formulated based on a particular player'sposition. For example, a quarterback fantasy number may use passattempts, completion ration, touchdown passes, and turnovers asstatistical input in deriving an overall fantasy number. In contrast, afantasy number for a cornerback may be based on the number ofinterceptions and tackles that the particular cornerback player had inthe course of a season.

Fantasy league services, particularly those that operate on theInternet, sometimes provide some predictive valuation of a player forpurpose of guiding members of the fantasy league to select players. Forexample, a fantasy league service may list a number of quarterbacks inthe NFL along with a fantasy number. The fantasy number may be based onthat player's past performance, particularly in respect to how thatplayer's past performance has been valued in terms of that league'spoint scoring system. A participant or member of a fantasy league mayalso subscribe to third party services, review fantasy sports magazines,or other sources of information to obtain statistical information forpredicting the value of a particular player to his fantasy team. Otherservices may quantify statistics in terms of one or more predictivevalue numbers in order to guide their users in selecting players fortheir fantasy teams. Current methodologies to evaluate players based onhistorical statistics are described in more detail below.

There are different types of fantasy leagues, each with different setsof rules. In general, the rules of the fantasy leagues may be based onthe amount of participation the user of the service is expected to havewith the fantasy teams. Some fantasy leagues are for casual users, whileothers are for more sophisticated users who spend considerable more timedeveloping strategy and data. But in most cases, fantasy leagues havetwo stages: (1) player selection, and (2) team management.

In player selection, the members of the fantasy league make playerselections based on predictions of which players are best suited fortheir team. A player from a sporting team is typically only assigned toone fantasy team, so a league member will not get every player he wants.Rather, league members may be required to rank players and have theservice assign players to each member. Alternatively, a live orsimulated draft may be conducted in which each league member makes aselection in a designated draft order. In either case, the fantasyleague members must make some decisions on what players to select andwhen. For example, the member may make a player selection based on whathe hopes that player will produce in terms of fantasy points, and whatplayers other members in the league are expected to select for theirrespective fantasy teams. Current methodologies to assist users inmaking draft decisions are described below.

After player selection, the members manage their teams. Many fantasysports services let members trade players, release players, and acquirenew players as free agents. However, certain league-dependent rules mayapply to these player transactions. For example, the number of trades amember may make may be limited in a season. The number of acquisitionsmay also be constrained by team size. Members may make managementdecisions based on the performance of their fantasy teams. For example,players performing poorly in terms of points may be released andreplaced. A member may prefer another player, in which case a trade maybe worked out amongst two members in a league. Again, the particulartransactions that can be done with a particular fantasy service dependon the rules of that fantasy service.

Fantasy leagues may include play-off and championship rounds, but theserounds may not be based on playoff or championship events in the actualsports league. For example, some fantasy football leagues designate thefirst 12 weeks of the NFL season the regular season, designate weeks13-16 of the NFL's regular season the playoffs, and ignore week 17, whenmany meaningless games are played. Many fantasy leagues end with week16, because the subsequent playoff season only involves a fraction ofthe players in the NFL.

Existing Player Valuation Methods

There are two basic metrics that are normally used to determine a valueassigned to a player for purpose of predicting that player's value in afantasy league. These metrics include performance categories and afantasy league point system. Performance categories include statisticalquantities of a player's past performances. For example, the performanceof individual offensive and defensive players are tracked in multiplecategories (i.e. Touchdowns, Yardage Gained, Receptions, Interceptions,Field Goals) and aggregated over an entire season. Defensive teams as awhole are also tracked in multiple categories (i.e. Touchdowns Against,Yardage Against, Interceptions, Sacks) and aggregated over an entireseason. Each individual fantasy sports league sets a specific pointvalue for each performance category (i.e. 6 points for a Touchdown, 1point for 20 yards gained, 2 points for a reception, 5 points for aField Goal over 50 yards). These two metrics are combined (e.g.multiplied together and summed) to compute an individual players orteams total estimated fantasy point value.

Existing Draft Methods

There are currently two methodologies used during a fantasy sports draftto help users make decisions on what players to select. The firstmethodology, Value Based Drafting (“VBD”), is the most prominent. Thesecond methodology, Dynamic Value Based Drafting (“DVBD”), is a moreadvanced version of VBD but is not widely used because of the complexityof the calculations. VBD provides a relative value of players incomparison to each other based on a fantasy point value metric beforethe draft starts.

VBD is used to determine which players are the most highly valuedrelative to other players at the same position. To determine whichplayer will provide the most relative value, VBD subtracts from allplayers at a position the value of the last starting player at thatposition based on player rankings (i.e. if there are 10 participants ina league that each have to start 2 Running Backs, then the point valueof the 20^(th) most highly ranked Running Back will be subtracted fromthe point value of all other players). This provides a relative value ofall players available in a draft as compared to the worst startingplayer at their same position.

DVBD uses VBD as its basis and also takes into account changes in thesupply of players at each position as the players are drafted. Theadvantage that DVBD provides over VBD is in calculating the relativevalues of players still available as a draft progresses. Instead ofusing the arbitrary value of the last starting player at each positionas a baseline, DVBDuses the value of the players likely to be availablefor the participants' next draft pick. This value is more pertinent asusers typically compare consecutive draft picks with each other moreoften than they compare their current draft pick with the last pick ofthe draft. This value will change as players are selected and as thedraft progresses.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a methodology for enabling a user of a gamesimulation to formulate a predictive valuation of one or more players ina sports league, according to an embodiment.

FIG. 2 illustrates a method in which dynamic player value parameters maybe used to as part of an overall strategy for a fantasy league,according to an embodiment.

FIG. 3 illustrates a player valuation module for determining playervalues in a fantasy league, according to an embodiment.

FIG. 4 illustrates a method in which players may be valued to facilitatea user's selection in a particular draft round, according to anembodiment.

FIG. 5 illustrates various parameters that can be used to value a playerat the draft stage of a fantasy league, according to an embodiment.

FIG. 6 illustrates a method for evaluating various draft considerationsin the course of a fantasy league draft, according to an embodiment.

FIG. 7 illustrates a network architecture for implementation ofembodiments described herein with a fantasy league.

In the drawings, the same reference numbers identify identical orsubstantially similar elements or acts. To easily identify thediscussion of any particular element or act, the most significant digitor digits in a reference number refer to the Figure number in which thatelement is first introduced. Any modifications necessary to the Figurescan be readily made by one skilled in the relevant art based on thedetailed description provided herein.

DETAILED DESCRIPTION General Overview and Terminology

Embodiments of the invention provide a system, method, and tool toevaluate and analyze players from real-life sports leagues for purposeof selecting players for use in fantasy teams and other gamesimulations.

An embodiment described herein facilitates a game simulation of a seriesof sporting events that occur over a duration of time. In oneembodiment, a plurality of parameters are associated with individualplayers in a series of sporting events. An input may be detected from auser of the game simulation. The input may indicate a preference of theuser for how one or more of the plurality of parameters are to be valuedin relation to one another. An overall value for one or moreparticipants in the game simulation may be indicated, where the overallvalue corresponds to a value of the plurality of parameters and to theinput.

The following acronyms may refer to the following: NFL (NATIONALFOOTBALL LEAGUE), NBA (NATIONAL BASKETBALL ASSOCIATION), NHL (NATIONALHOCKEY LEAGUE), MBL (MAJOR LEAGUE BASEBALL), MAJOR LEAGUE SOCCER (MLS),and NASCAR (NATIONAL ASSOCIATION FOR STOCK CAR AUTO RACING).

The expression “series of sporting events” may refer to a planned seriesof contests for a particular sports league or organization. In the NFL,for example, a regular season (excluding a playoff season or anexhibition season) includes 16 games played over a period of 17 weeks.

A “game simulation” of a sporting event series refers to anyrecreational and predictive analysis of an aspect of a sporting eventseries. Examples of game simulations include fantasy leagues for seasonof the NFL, the NBA, or MBL. A specific aspect of a sporting eventseries may include a particular player in the sporting event. Forexample, the player may be a football Quarterback or Running Back in theNFL.

A player or participant in a sports league may include a football player(NFL), a basketball player (NBA), a baseball player (MBL), or a hockeyplayer (NHL). The concept of a player or participant may be extended toa team and not just to an individual. In the context of autoracing, theparticipant may include a driver, a driving team, and/or a set of racingcars that belong to one team or are associated with one another (e.g.“Team Porsche”).

The expression “strength of schedule” means, for a given participant, adetermination of a competence level of one or more opposing participantsand/or opposing participant teams. Various methodologies may be used todetermine the competence level of the opposition, some of which aredescribed in more detail below. The opposition may be consideredindividually or in aggregate. In football, for example, theparticipant's team may have a strong strength of schedule rating if theparticipant's team plays more than its share of winning teams.

As used herein, the term “simulation” means a statistical simulation, ora simulation that is based at least in part on a statistical simulation.

In an embodiment, a system, method, technique or tool is provided tofacilitate analysis of a game simulation of a series of sporting eventsthat are to occur in an upcoming season. For example, the gamesimulation may be a fantasy league for an upcoming season of aprofessional sports league. An embodiment provides that a plurality ofparameters are associated to each of a plurality of participants in theseries of sporting events. The plurality of parameters may include atleast a performance-based parameter and a non-performance-basedparameter. A user-specific input from a user of the game simulation maybe detected. The input may indicate how one or more of the plurality ofparameters, including the non-performance-based parameter, are to bevalued in relation to one another. An overall value may be indicated forone or more participants in the game simulation that is to a value ofthe plurality of parameters and the input.

The performance parameters may correspond to metrics that represent aquantity (such as a statistic) that is based on achievement of aplayer's performance. For example, in football leagues such as the NFL,performance parameters may correspond to metrics such as number oftouchdowns scored, how many passes were caught or completed, how manyyards were rushed, number of fumbles lost etc. The performanceparameters may also be based on a combination of two or more performanceparameters, such as the Quarter Back Rating. A non-performance parametermay correspond to a measure (such as a likelihood) that may affect agiven player's performance. Examples of non-performance parametersinclude strength of opponents scheduled (such as strength of opponent'sdefense for offensive players, and strength of opponents offense fordefensive players); consistence, and durability. Consistency anddurability may be based on historical data. Consistency may, forexample, measure deviation amongst performance parameters from game togame in past performances of a player, while durability measures thenumber of times a player has been injured, or for how long etc. Otherexamples of non-performance parameters include measurements for weatheror environmental impact. A specify type of non-performance parametersdescribed in detail below are dynamic player value parameters.

According to another embodiment, a predictive value may be determinedfor a given player in a sports league. The predictive value may be basedon one or more individual games that the given player is scheduled toplay or otherwise perform in. The predictive value may be based at leastpartially on an analysis of the given player playing in each of the oneor more individual games. An overall value of the given player may beindicated using the predictive value of that player for the individualgames.

According to other embodiments described herein, players of a spectatorsports league may be evaluated for a draft in a fantasy league. In oneembodiment, an overall value of a player of the sporting league isdetermined based at least in part on a past performance of the player. Agiven participant of the fantasy league may be indicated an adjustmentto the overall value of a particular player in relation to other playersin the sporting league based on another player that the givenparticipant has already selected for the given participant's fantasyteam. In another embodiment, a comparison may be maintained forindividual players of the sports league, where the comparison indicatesa difference in the value of that player with respect to some referencevalue. The reference value may be another player.

Currently, fantasy football (based on the NFL) is the most popular formof game simulation. For this reason, some examples of embodimentsprovided in the specification are explained in the context of football.However, embodiments described herein may be equally applicable to othersports, including baseball, basketball, hockey, soccer, golf, cricket,autoracing, bowling, horse racing, tennis, poker and chess. Furthermore,embodiments described herein may apply to any league or organization inwhich sports or played and statistics are maintained, includingnon-professional sporting events such as collegiate sports or Olympicevents.

Embodiments described herein may be implemented as acomputer-implemented method. The term “computer-implemented method”refers a method that has at least some, but not necessarily all of itssteps performed through execution of instructions by one or moreprocessors. Individual steps of a computer-implemented method may beperformed programmatically, meaning a given step may be performedautomatically through the execution of programming code or logic.

Embodiments of the invention may also be implemented over a network,such as in conjunction with a network service that operates a gamesimulation such as a fantasy league.

In addition, embodiments of the invention may be implemented through useof modules that communicate to users through use of a network. Forexample, an embodiment may be implemented as an integrated, additionalor supplemental service with a network operated fantasy league, such asprovided on the YAHOO! Network or CBS SPORTSLINE. Alternatively, anembodiment may be implemented as a stand-alone application which a userof a fantasy league or other game simulation may use to enhance hisperformance. In one implementation, one or more module may be used toevaluate players for a game simulation, and to enable users of the gamesimulation to make draft picks. As used herein, a module includes aprogram, a subroutine, a portion of a program, a software, firmware orhardware component that is capable of performing a set of tasks orfunctions. A module can exist on a hardware component, such as a serveror client, independently of other modules. Alternatively, a module canexist with other modules on the same server or client, or even withinthe same program.

Player Valuation

One application for an embodiment is a system or tool for use with afantasy sports league, such as a fantasy football league for the NFL, ora fantasy baseball league for the MBL. In the context of suchsimulations, embodiments described herein enable a user participating inthe simulation to make more informed and/or strategic decisionsregarding player/participant selection and team management.

A general format for a fantasy league includes (1) a team formationstage that gives a user an opportunity to make his player selections forhis fantasy team, and (2) a team management phase where a user can viewthe performance of his fantasy team and make certain managementdecisions based on the results. The particular management decisions thatare allowed are based on the particular rules of the fantasy league. Thefantasy team may have designated positions similar to the sporting eventthat it simulates. Players are designated to positions in a givenfantasy team irrespective of their actual team assignment in the sportsleague. It is typical for a fantasy football team, for example, to havea quarterback from one NFL team, and a Running Back from another NFLteam. The exact number of positions, including backup positions that areto be assigned to a fantasy team are determined by the rules of theparticular fantasy league.

A fantasy league normally consists of a set number of fantasy teams.Each of the fantasy teams may be controlled by a user (sometimesreferred to as “team owner”). Generally, the number of teams that canparticipate in a league are set by the rules of the fantasy league. Forexample, many leagues set the number of teams per league at 8, 10, 12 or20. Typically, at the start of a league, all the teams are empty. Adraft or player/participant selection stage is performed, where fantasyteam owners make player selections that result in the fantasy teamsbeing populated. There are different draft rules that can be used togovern the draft order amongst the team owners during a player draft. Asimple fantasy league may have users prioritize their list of players,and then randomly distribute players from the real sports league basedon user-selections. When two or more users select the same player, aprocess makes the decision on which user receives the player based onfactors such as a user's priority for that player and randomness. A moresophisticated fantasy league may have a live or simulated draft, whereeach user's take turns making draft selections. In this scenario, thedraft selections are made from an available pool of players. The orderin which the first round and subsequent rounds of the draft areconducted are important to the users, as the draft order is whatdetermines the user's ability to select a particular player.

Embodiments of the invention enable a participant of the game simulationto evaluate real-life players/participants from a spectator sportsleague for purpose of assigning select players to his fantasy team. Whenplayers are selected for the fantasy team, statistical valuescorresponding to the performance of that player in the sports league arevalued in the fantasy team. The performance of the fantasy team is basedon the statistical performance of the real-life players in the sportsleague. An embodiment described herein facilitates the fantasy leagueuser in evaluating players of the sports league using, at least in part,a sophisticated statistical analysis of various factors relating to theplayer's past performance. These factors assist in predicting aparticular player's success on the fantasy team. Through use ofembodiments described herein, a player may use strategic considerationsto assign his own configuration, weighting or valuation of the variousparameters that comprise the player valuation. In addition, events inthe sporting event series, such as the passing of a week in the NFLseason, may be used to update the parameters of the player valuation onan ongoing basis.

According to another embodiment, a system is provided to assist a userin making draft picks during the team formation stage of a fantasyleague. A system such as described takes into account factors such as(i) what draft picks the user will have (given a particular draft order,rounds, and picks per round), (ii) how selection of a particular playerwill compare to another user's selection of another player, (iii)scheduling conflicts and issues that may affect a player's value to auser, and (iv) ramifications of a user passing on a particular player ina round in view of the user's subsequent order of picks.

FIG. 1 illustrates a methodology for enabling a user of a gamesimulation to formulate a predictive valuation of one or more players ina sports league. The predictive valuation is a measure of how aparticular player is expected to do in terms of fantasy points. Theplayer valuation may be utilized to facilitate a decision in a playerselection stage of the fantasy league. Alternatively, the playerselection may be used during the team management stage to facilitate adecision on what player should be selected as a replacement, or made thebasis of a trade. For example, in some fantasy football leagues, a usermay make a “free agent” acquisition from a pool of available players inorder to replace an injured player. A tool or system described under anembodiment enables the user to make a decision of what player in thefree agent pool is best for him, based on statistic analysis that isconfigured to the user's particular strategic considerations.

For illustrative purposes, embodiments described herein make referenceto a fantasy league as a particular example of a game simulation.Because of its overwhelming popularity, examples emphasize fantasyfootball leagues, but it should be apparent that embodiments andexamples described below are equally applicable to other sports andsports leagues, as well as to other forms of game simulations for suchsports.

According to a method such as shown by FIG. 1, a step 110 provides thatone or more parameters for evaluating players in a spectator sportsleague (or other organization or forum) are defined. The parameters maybe selected based on known information about the sports league that issimulated, including, for example, statistical quantities that indicatea player's value, competence, or skill level. The parameters may bedefined by anyone or all of the following: (i) a host or service thatprovides or otherwise operates the game simulation, (ii) a third-partyservice that operates with a particular game simulation to provide theanalysis described herein, (iii) inherently, within a tool that operateson processing resource to compile statistics and other information for agame simulation (see e.g. player valuation module 710), and (iv) by theuser, on the fly and/or in a predetermined fashion.

Fantasy leagues, for example, operate at different levels ofsophistication. An average level of user-participation in an advancedfantasy league may be much greater than that of a more casual league.Specifically, the parameter definitions for an advanced fantasy leaguemay be more sophisticated, numerous and/or involved than the parameterdefinitions of a more casual fantasy league.

One type of parameter that can be identified is a performance parameter.A performance parameter may correspond to any statistical orquantitative category of a player's past performance. For example, inthe sport of football, each position may have different parameters. ForQuarterback, the parameters may include passing yardage, completionrate, the number of interceptions thrown, and the number of touchdownpasses thrown. For a Running Back, the parameters may include rushingyardage, number of attempts, the number of passes caught, passingyardage, number of touchdowns scored, and the number of fumbles. In thesport of baseball, the parameters may correspond to batting average,home runs, extra base hits (doubles, triples), walks, strikeouts, anderrors. For pitchers, the performance parameters may include number ofwins, number of losses, the pitcher's earned run average, the number ofstrikeouts, the number of innings pitched, the number of saves, and thenumber of blown saves. In basketball, the parameters may correspond topoints scored, shooting percentage, assists, rebounds (both offensiveand defensive), turnovers, free throw percentage, and three-pointpercentage. For the sport of hockey, the performance parameters mayinclude goals scored, shots on goal, and penalty minutes. In golf, theprevious scores of a particular golfer may be calculated. Inauto-racing, vehicle speed and lap times of previous races may be usedfor performance parameters. The aforementioned are just a small sampleof all the performance parameters that can be defined to describe theperformance level, skill level, or past results of participants insports leagues and organizations. Types of statistical categories anddefinitions that are considered under embodiments of the invention areprovided in various publications, including for example: Official 2003National Football League Record & Fact Book (Official National FootballLeague Record and Fact Book), and the Sports Illustrated 2004 Almanac;Tuff Stuff's Fantasy Football Guide (2003 edition), and RotoWorld.com'sBaseball Reference Guide (2004 edition). All of the aforementionedpublications are hereby incorporated by reference in their entirety forpurpose of their respective teachings in the definition and use ofstatistical and quantitative parameters (batting average, rushing yards)of professional and other spectator sports leagues.

In addition to performance parameters, an embodiment provides foridentification and use of parameters that consider player values for anupcoming season on a game-by-game basis. This type of parameter istermed a dynamic player value parameter. This class of parameters buildson the current player valuation methodology by adding metrics that moreaccurately predict the true week-to-week value of a player to a fantasysports team. These metrics enable users to account for the dramaticvariation in performance over an entire season. For example, upcomingscheduled games of a team in a real-life sports league may be analyzedfor attributes or factors that indicate a likelihood that a player'svaluation will be more accurately reflected by a consideration ofindividual games in a player's schedule, rather than a predictiveevaluation of how a player will perform over an entire season. In thisway, a dynamic player value parameter may be predictive of a player'svalue to a fantasy team based on a game-by-game analysis of thatplayer's schedule.

One example of a class of dynamic player value parameters is a parameterthat quantifies a strength of opponent's defenses to a particularplayer. Such a parameter can be valued to predictively quantify thestrength of individual opponent's defenses for each player's team in agiven segment of the season. This parameter may be used to evaluate anoffensive player, such as a Running Back or Quarterback. Another exampleof this type of parameter is a strength of opponents' offense, whichpredictively quantifies the strength of individual opponent's defensesfor each player's team in a given segment of the season. This parametermay be useful in evaluating a defensive player, such as a linebacker orsafety. Both of these examples are described in greater detail below.Numerous other examples exist in addition to the examples providedabove. In the sport of baseball, a strength of opponent's pitching forscheduled games may be used to evaluate how likely a player will dobatting in a given stretch of games. In order to evaluate pitches,parameters that evaluate how well that pitcher's opponents hit or howoften they strikeout may be used.

Step 120 provides that a weighting factor for the different parametersis detected or otherwise determined for a user of the fantasy league.The weighting factor may be determined in anyone of a variety of ways.In one embodiment, the weighting factor may be determined directly via auser-input. For example, the user may specify anyone of the followingwhen determining the fantasy player value of an offensive player: (i)maximize the weighting of a parameter value corresponding to thestrength of opponent's defenses; (ii) minimize the weighting or ignorethe parameter value corresponding to the strength of opponent'sdefenses; (iii) formulate the weighting so that it is a bonus or penaltyfor the player based on the value of the parameter. In addition, theweighting may be varied. For example, some parameters may be given moreweight by the user for purpose of selecting players via the draft, whileother parameters are given more weight when selecting players via freeagency.

The weight factor may also be determined indirectly. For example, theuser may submit one or more profile items, which can then be determinedand correlated to a particular profile for the user of the gamesimulation. As another example, the user may be profiled as arisk-taker, in which case the weighting may be biased towards favoringhigh-risk/high yield parameters. Alternatively, the profile items may bedetermined for the user based on the user's behavior. For example, theuser may be classified or profiled by age group or behavior, such as inthe context of online activity.

In step 130, a set of player values are determined for individualplayers in the sports league. The set of player values may correspond toone metric that combines the values of the different defined parameterswith the user-specific weighting factors. Alternatively, the set ofplayer values may be multi-dimensional, in that multiple metrics about aparticular player are separately presented to the user.

In step 140, users are presented with associated sets of parametervalues for selection of their fantasy team. The sets of parameter valuesmay be displayed, or otherwise communicated to the user in order tofacilitate that user in making a player selection. The manner in whichplayer selections are subsequently made depend on the rules of thesimulation.

FIG. 2 illustrates a method in which dynamic player value parameters maybe used to as part of an overall strategy for a fantasy league. Anembodiment such as described may enable players to evaluate playersdifferently for certain games or portions of the season for the sportsleague. Users of a fantasy league may seek to benefit by using astrategy that is skewed to account for seasonal or game performances ofcertain players. For example, an embodiment such as describedfacilitates analysis of players for game simulations that run forindividual games, or portions of the seasons. Alternatively, anembodiment as described in FIG. 2 enables the user of the gamesimulation to strategize player acquisition and management decisionsbased on specific games or time periods in the season. For example,players that tend to start strong and taper may be identified andselected for trade value during the player acquisition trade, forpurpose of being able to trade that player once the season progresses.

Step 210 provides that a portion of the season for the sports league isdesignated. The portion of the season may correspond to a specific game,a specific set of games, or a portion of the season for the sportsleague. A fantasy football league, for example, may operate in five gamesets over the course of a 17 week football season. Alternatively, a userof a fantasy league may decide to look for players that are mostvaluable at the beginning of the season, or at the end of the season.Thus, the designation of the season portion may be set by a duration ofthe fantasy league, or by strategic considerations of the user.

In step 220, dynamic player value parameters are defined that, for agiven player, provide a player value that is based at least in part on avalue of a performance parameter that is more indicative of the value ofthe given player for the season portion, as opposed to the entireseason. For example, the performance parameter value may predict anoverall player value to be disproportionately impacted by the particularseason segment than the season in aggregate.

In step 230, a player valuation for the portion of the season ispresented for a particular user based at least in part on the parametersdefined in step 220. For example, players may be ranked for a particularsegment of the season, such as the very beginning or very end of theseason. Alternatively, different users may be provided differentvaluations, even for the same set of players, based on weighting inputprovided by those users.

Dynamic Player Valuation Examples

The following are examples of dynamic player value parameters, such asdescribed and/or used with embodiments of FIGS. 1 and 2. In anembodiment, the dynamic player values may be used to determine a valueof a particular player to a fantasy league based on a predictivegame-by-game analyses of the upcoming season for that player.

A durability parameter may be defined to correlate to a player'sdurability. A value for a durability parameter may predict or indicatethe likelihood that a player will miss games in a season due to injury.Injuries to players in the sports-league can make a significant impactto a fantasy team's performance. In an embodiment, as player'sdurability parameter is valued based on the average number of games notplayed due to injury during a season. One manner in which durability canbe quantified is normalize the parameter from −1 to 1; with −1representing the least durable players and 1 representing the mostdurable players. This normalized value is multiplied by (i) auser-defined weighting (percent of player value) for player durability(step 120 of FIG. 1) and (ii) the players point value (based on acompilation of other factors, including performance), to arrive at thefinal value for this metric. This final value is added to the fantasypoint value of each player, as determined from performance parametervalues from past performances. The result of this algorithm is toincrease, or reward, the fantasy point value of players that are lesslikely to get injured and decrease, or penalize, the fantasy point valueof players that are more likely to get injured. When configuring thismetric, users are able to select from (i) reward and penalize playersbased on this metric, (ii) only reward players based on this metric(this rewards players with a normalized value greater than zero), or(iii) only penalize players based on this metric (this penalizes playerswith a normalized value less than zero). A durability parameter valuemay be considered in aggregate for all of the season, or be considered afactor for a specific portion of the season. For example, if thedurability parameter predicts a likelihood of injury, the user of thegame simulation may elect to (i) consider the parameter value inaggregate for the entire season, and/or (ii) consider the parametervalue for only a portion of the season where the player is more likelyto be injured (such as at the end of the season). Therefore, if theplayer is injured in the second half of a season, his performance in thefirst half of the season (or conversely in the second half of theseason) disproportionately impacts his aggregate fantasy point value forthe entire season.

A consistency parameter may be defined based on a consistencymeasurement of the player. A value of a consistency parameter maypredict how constant a player's performance in a sports league will varyfrom game to game. Depending on the manner in which a fantasy league,for example, calculates player valuations, consistency may determinewhen one player's overall or aggregate parameter values or not a trueindication of the player's valuation to a particular fantasy team. In anembodiment, a player's consistency is defined as being based on thestandard deviation of their performances over the span of theirprofessional career. These standard deviations are then normalized from−1 to 1; with −1 representing the least consistent players and 1representing the most consistent players. This normalized value ismultiplied by (i) a user-defined weighting (percent of player value) forplayer consistency (step 120) and (ii) the players fantasy point value,to arrive at the final value for this metric. This final value is addedto the fantasy point value for each player (step 130). The result ofthis algorithm is to increase, or reward, the fantasy point value ofplayers that are more consistent with their performance and decrease, orpenalize, the fantasy point value of players that are less consistentwith their performance. A user may enter a negative number for theweighting for player consistency in which case players would be rewardedfor inconsistent performance and penalized for consistent performance.When configuring this metric users are able to select from (i) rewardand penalize players based on this metric, (ii) only reward playersbased on this metric (this rewards players with a normalized valuegreater than zero), or (iii) only penalize players based on this metric(this penalizes players with a normalized value less than zero).

Another parameter may correspond to a strength of opponents defenses.Such a parameter may be applied to offensive football players, such asthe Quarterback, Running Back and Wide Receiver. Each player's team mayhave a schedule that is accessible to the fantasy league member. Theschedule for the team of a particular offensive player may be evaluatedto determine the Strength of Opponents Defenses for a particular game orset of games. Offensive players typically perform better against weakerdefenses and worse against stronger defenses. The Strength of OpponentsDefenses parameter enables a user to easily account for this potentialvariation in performance. The Strength of Opponents Defenses parametermay be determined for individual games or on an aggregate basis for agiven set of games. According to one implementation, the Strength ofOpponents Defenses can be determined for (i) all weeks of the season(can be configured to include or exclude week 17), (ii) the weeks of thefantasy leagues playoffs (can be configured to include any combinationof weeks including week 13 and beyond), and (iii) the first five gamesof the season (this is not configured). The time period used may bebased on the user's wishes, or on rules of the simulation.

All Game Scenario: The total yards-against of each team defense anoffensive player plays against during each week of the fantasy footballseason is aggregated for each player. It should be noted that there aremany metrics to quantify the strength of opponents. In this context, forexample, other metrics for quantifying the strength of opponents includepoints scored against, passing yards thrown against, and rushing yardsgained against. In one embodiment, the aggregate is based on data fromone or more previous years, for each team involved. In anotherembodiment, the aggregate yards-against number is determined for eachteam on an ongoing basis for a current season of the football league.According to one embodiment, the aggregated values are then normalizedfrom −1 to 1; with −1 representing the best grouping of opposingdefenses (least aggregate yards against) and 1 representing the worstgrouping of opposing defenses (most aggregated yards against). Thisnormalized value may be multiplied by (i) a user-defined weighting(percent of player value) for a season segment (see FIG. 2) and (ii) theplayer's fantasy point value as determined by performance parameters andother valuations. The final value may be added to the fantasy pointvalue to derive a new number. The result of such an algorithm is torecognize (i) additional value for offensive players that play againstpoor defenses during a season segment, (ii) a decrease in value foroffensive players that play against good defenses during this seasonsegment. A user may adjust the weight of this parameter favorably orunfavorably based on other considerations. This particular parameter maybe configured by a user in one of the following ways: (i) reward andpenalize players based on this metric, (ii) only reward players based onthis metric (this rewards players with a normalized value greater thanzero), or (iii) only penalize players based on this metric (thispenalizes players with a normalized value less than zero).

Playoff Game Scenario: The total yards-against (or an alternativemetric) of each team defense an offensive player plays against duringeach week of the fantasy football playoffs is aggregated for eachplayer. The aggregated number may be based on the current fantasyfootball season (e.g. the first 12 games of the year). The aggregatedvalues may then be handled in the manner described with the previousscenario—that is they may be normalized from −1 to 1; with −1representing the best grouping of opposing defenses (least aggregateyards-against) and 1 representing the worst grouping of opposingdefenses (most aggregated yards-against). Similar to the previousscenario, the normalized value is multiplied by (i) a user-definedweighting (percent of player value) for this season segment (such asdescribed in the following step and with FIG. 2) and (ii) the player'sfantasy value from the regular fantasy league season. The result is thefinal value metric, which can be used to reward and/or penalize players,as described with the previous scenario.

Season Segment Scenario: Due to strategy or other considerations, a usermay decide to determine the parameters for certain segments of theoverall season. For example, the user may decide to use the first fiveweeks of the football season to develop a fantasy player for a tradewith another player. In such a scenario, the Strength of OpponentsDefenses parameter may be determined for the teams of select players inorder to identify players that are likely to have strong performances inthat season segment.

Another parameter may correspond to strength of opponents offenses. In,for example, the context of football, this parameter may be used toadjust the fantasy point value for defensive players and defensive teamsbased on the strength of their opponents offenses that they play againstduring certain segments of the season. Defensive players and teamdefenses typically perform better against weaker offenses and worseagainst stronger offenses. These metrics enable users to easily accountfor this potential variation in performance. The season segments include(i) all weeks of the season (can be configured to include or excludeweek 17), (ii) the weeks of the fantasy leagues playoffs (can beconfigured to include any combination of weeks including week 13 andbeyond), and (iii) the first five games of the season (this is notconfigured). This parameter may be used similar to Strength of OpponentsDefenses, described above, for the different segments or portions of aseason.

Player Valuation Module

One or more embodiments described herein may be implemented in the formof a module or program. The module may be accessible to users over anetwork, or alternatively in the form of a client application, so as toserve as a tool to facilitate the participants in the fantasy league.FIG. 3 illustrates a player valuation 310 module for determining playervalues in a fantasy league. In an embodiment, the player valuationmodule 310 may operate on one or more computer systems, and includesinstructions executable by one or more processors.

The player valuation module 310 may process different types of data andinformation, combined with user-input, in order to determine a set ofplayer valuations 322. The player valuations 322 may be made availableto all users. However, the player valuations 322 may be configured forindividual users, so that different users of the module receivedifferent player valuations. Alternatively, player valuation module 310may be executed by users individually, so that the payer valuations 322are made available only to the users who use the module.

In an embodiment, player valuation module 310 receives a player list312. The player list 312 may correspond to a complete or partial list ofall players in a particular sports league at a particular time or timeperiod. For example, the player list 312 may correspond to a list ofplayers on each team in the NFL after the preseason is over and all ofthe teams have been set. The player valuation module 310 may processhistorical statistical data 316 on past performances of individuals inthe player list 312. For a quarterback in football, for example, thehistorical statistical data 316 may include anyone or more of thefollowing: quarterback rating, passing yardage, completion rate,yards-per-attempt, interceptions thrown, and touchdowns thrown. Thehistorical statistical data 316 may also include data that is notspecific to a particular player. For example, the won-loss record ofeach team, or aggregate statistics on teams as a whole, may be compiledin order to cross-reference player specific data with the respectiveplayer's team data.

In an embodiment, player valuation module 310 also receives futureupcoming season information 318 for determining values of forwardlooking event specific parameters. In an embodiment, this type of dataincludes data for defining and valuing dynamic player values. This typeof data includes, for example, the schedules of the teams for eachplayer that is in player list 312. The historical statistical data 316and the season information 318 may be gathered from informationresources, including libraries or databases. For example, playervaluation module 310 may operate on a terminal that accesses a library,database, or service over a network in order to gain the historicalstatistical data 316 and season information 318. Alternatively, some orall of the data and information may be received manually, from anoperator of the fantasy league, an operator of the player valuationmodule 310, or from a user in the fantasy league.

According to an embodiment, player valuation module 310 also receivesconfiguration input from user(s) of the fantasy league. An example ofconfiguration input includes weighting input 326 that weights howdifferent items in the historical statistical data 316 and the futureinformation 318 should be valued with respect to one another. In anembodiment, the player valuation module uses the data and information ina manner described with embodiments of either FIG. 1 or FIG. 2. Thus,values parameters reflecting performance of a player and forward-lookingevents are determined based on the data and information. Specificexamples of what can be determined for a particular player in the sportsleague include performance parameters, schedules, and metrics such asstrength of schedule, durability and consistency. The weighting input326 may determine what parameters a user thinks is important for aparticular player or set of players, and how much some parameters shouldbe valued over others. For example, the user may wish to weightdurability to penalize the player value of a player because of theirstrength of schedule is strong. Specific examples of how weighting input326 can be used are described with FIG. 1. Because each user in thefantasy league may have a different set of configuration input, oneembodiment provides that the player valuation module 310 accepts inputfrom different users, and maintains for each user in the fantasy leaguea different set of player valuations 322. Thus, different users in thesports league may have different player valuations for the same playerin the sports league. Since each user of the fantasy league can rely onhis own configuration input, the user is able to skew player valuations322 based on his individual strategy. This compares favorably toexisting fantasy sports leagues, which typically provide the same playervaluation numbers to all the users, so that users make decisions whilebeing presented the same set of data.

In addition to configuration data, player valuation module 310 may useother kinds of data 328. For example, weather information may be enteredas input or accessed automatically by the player valuation module 310.Examples of weather information include historical weather data andforecasts. A user may decide to use weather information, or even toweight it. For example, the user may rely on weather information indeciding whether to select a player who is likely to play in inclementweather for a significant portion of the season. This type of data mayconsider how, for example, certain external factors may influence aparticular class of players. For example, with respect to weather orenvironmental conditions, a Kicker or Running Back in football may havea higher metric, at least for this particular consideration, if thatplayer plays indoor games in winter, or is located in a tropicalclimate. An offensive player on a football team with an indoor stadiumand Astroturf may have a higher consideration than a player who is on ateam that plays outdoors and in the Northeast of the country.

Draft Selection

In many fantasy leagues and other game simulations, the player formationstage serves as a foundation for the user's fantasy season. In mostfantasy leagues, for example, the player formation stage is when theuser forms the basis or core of his team. Even the subsequent ability orneed for the user to make additional player acquisitions depends on thequality of the draft. For example, the user may reduce his need forhaving to waive non-performers in favor of free agents, or enhance hisability to make a trade for another player.

As previously mentioned, the team formation stage is called a draft inmany fantasy leagues. An embodiment such as described in this sectionmay have particular relevance to fantasy leagues in which the draft isconducted round by round, with each round having a particular draftorder determined by rules of that fantasy league. A fantasy leaguegenerally consists of a number of teams in the neighborhood of 8-20(although it is possible to have more or fewer). In more sophisticatedleagues, each team gets one pick in a particular draft round. The orderof selection in each draft round is determined by the fantasy leaguerules. A common way to conduct a draft is to randomly select the firstorder, then reverse that order for the next round, and then reverse itback for the third round until all the player positions in each teamhave been selected. For example, Table-1 is one common example of how adraft order may be carried through several rounds:

TABLE 1 Snake Draft Rule TEAM 1^(ST) 2^(ND) 3^(RD) 4^(TH) ID ROUND ROUNDROUND ROUND A 1 6 (12) 1 (13) 6 (24) B 2 5 (11) 2 (14) 5 (23) C 3 4 (10)3 (15) 4 (22) D 4 3 (9) 4 (16) 3 (21) E 5 2 (8) 5 (17) 2 (20) F 6 1 (7)6 (18) 1 (19)Table 1 illustrates the snake draft rule where the player who has thefirst pick in the first round gets the last pick in the last round.Numbers inside the parenthesis designate the overall draft pick.Numerous variations to this basic scheme are possible. For example, thesecond and third rounds may have the same draft order.

FIG. 4 illustrates a method in which players may be valued to facilitatea user's selection in a particular draft round. A method such asdescribed by FIG. 4 may be performed by anyone or more of the following:(i) a service that conducts the fantasy league, (ii) a third-partyservice or tool that is available to all users of the fantasy league, or(iii) a personal tool used by one or more users in the leagueindependently of other users.

Step 410 provides that player values are calculated for each player inthe real-life sports league, or at least each player that is likely tobe selected. The player value calculations may be based on differentparameters, and in particular, performance parameters.

In step 420, player rankings are formed that consider select number ofavailable players. An embodiment such as described in FIG. 5 providesthat player rankings at the draft stage consider various parameters andmetrics, with a specific category of parameters that are based on draftconsiderations. The player rankings may be sorted by player position.For example, quarterbacks may be ranked based on their respectiveoverall valuations. According to an embodiment, there may be two or morecomponents to the overall player valuation. One component reflects theplayer's overall value, absent any draft consideration (fantasy pointvalue and dynamic point value in FIG. 5). Another component reflectsdraft considerations (draft value points in FIG. 5). With respect to thefirst component, the overall player valuations may be determined in amanner consistent with embodiments described with FIGS. 1-3.Alternatively, player valuations may be determined using moretraditional fantasy point value systems. With respect to draftconsiderations, an embodiment provides that players valuations take intoaccount whether the player selection will maximize the fantasy teamsoverall performance as compared to other teams in the fantasy league.This decision does not necessarily correspond to selecting the playerwith the highest value or player ranking. Rather, decisions may beimpacted by one or more factors such as (i) comparative value of playersavailable as opposed to other users for use in the fantasy league, (ii)the ability to draft quality players in subsequent draft rounds impactthe decision on draft picks. (iii) schedule conflicts amongst players,and (iv) sports team conflicts. These factors are reviewed in greaterdetail with FIG. 5. After each draft pick, or round, these factors willalso change. Specifically, the factors will change because the pool ofavailable players changes. In many cases, so does the draft order.

In step 430, a draft round is conducted. In the round, each user in theleague may make a selection. In step 435, a determination is made as towhether the draft is over. Once the draft is over, the method iscompleted in step 440. If additional rounds remain, then step 450provides that the parameters categorized as draft considerations areupdated. In an embodiment, the update considers the following for aparticular fantasy team: (i) the pool of available players after oneround in the draft, (ii) the players that are already selected for thatteam; (iii) differences amongst available players; and (iv) changes inthe draft order (see Table 1). The method then returns to step 420.

FIG. 5 is a block diagram that illustrates how players may be valued andselected in a player draft of a fantasy league, under an embodiment. Inparticular, FIG. 5 illustrates draft considerations which may be valuedas parameters in evaluating what player should be selected with aparticular draft pick. In a draft, player valuation may be based onthree general categories: (i) a first category 510 for fantasy points,which uses expected performance parameter values to value a player; (ii)a second category 520 of dynamic player value parameters, based onfuture, game-specific considerations; and (iii) a third category 530 ofdraft value considerations. Each category may comprise differentparameters. User-specified weight parameters may affect how categories,individual components and/or parameters of individual categories arevalued in relation to one another. An embodiment such as shown by FIG. 5enables a user to make a draft pick using considerations provided in allthree categories. This allows the user to properly consider the value ofa candidate draft pick based on past performance of that player,game-specific considerations in the upcoming season, and considerationsfrom the draft itself.

The first category 510 of fantasy points may correspond to standardpoint valuations given to players based on expected performances. Anoverall valuation number or indication may be formed based on one ormore parameters of expected performance. In one embodiment, componentsof this category include categories 512 of performance parameter valuesand a fantasy league point system 514.

The second category 520 includes dynamic player value parameters such asdescribed with FIGS. 1 and 2. These values indicate the value a playerwill bring to a fantasy team when games that the player is scheduled toplay in are considered individually, such as parameters of opponentstrength 522, consistency 524, and durability 526.

The third category 530 includes parameters of draft considerations. Onedraft parameter of the player role 532 pertains to whether a startingposition has been completely filled. In fantasy leagues, the performanceof a backup player is usually devalued in relation to a starter. This isthe case even if the backup player is actually a starter in the sportsleague. This parameter can be used to automatically inform the user whena position on a fantasy team has been filled. The result is that theuser can choose players who will be starters, and can value a starterover a backup, regardless of position. In many fantasy leagues, thedraft is conducted quickly, and each team consists of one participants.A tool that tracks when starter positions have been filled on a team hasvalue in that it affords the user an opportunity to track informationthat affects basic player-selection decisions.

Another draft parameter that can be tracked and maintained as a draftprogresses is a team parameter 534. The team parameter 534 may be valuedto track a degree to which available players are on the same sportsteams. If a fantasy team has too many players on one sports team, theuser may be taking an unplanned risk. For example, an unexpected badseason by that team may detrimentally affect the performance of allplayers on that team.

Both of the role parameter 532 and the team parameter 534 draftparameters provide examples of instances when, as the draft progresses,the user can devalue a particular player because of that user's previousdraft pick. For example, in football, once the fantasy user selects afirst quarterback, a system such as described automatically devaluesremaining quarterbacks, because the other quarterbacks would serve onlybackup roles on the fantasy team (and by most fantasy rules, fantasypoints from backups are not worth as much). As the draft progresses andthe teams fill out, the ability to automatically monitor this aspectbecomes valuable, particularly when users have limited time to make adraft pick. As another example, once a player selects a set number ofplayers from one team, or from an offense or defense of a team, otherplayers that are still in the draft pool may be devalued for thatplayer. Thus, for example, the fantasy points associated or displayedalong with a player may be reduced as a result of the user havingpreviously selected a certain number of players from the same team.

Other types of draft parameters are contemplated. In particular, a VBDparameter 536 and DVBD parameter 538 associate an opportunity cost witha particular draft selection. The VBD parameter 536 may be based on aVBD methodology for each player position. For example, for a draft roundconsisting of six picks, an embodiment provides that all players areranked by position. Each of the top five players in each position arecompared to the sixth best player in that position. This lets user'sdetermine a cost associated with a particular pick. For example, if theplayers at a particular round of the draft have parity, while at anotherposition there is considerable variance in terms of value, a parameterdetermined from the VBD methodology may conclude that there is too muchcost associated with picking up a player in the first position wherethere is parity. The correct decision may be that even though theavailable player at the second position is not as valuable in terms ofpoints relative to the first player, the second player is the betterdraft pick because of the drop-off in quality from one player to thenext at a particular position.

TABLE 2 VBD tally for Quarterback position in fantasy football.VBD_(QB1) VBD_(QB2) VBD_(QB3) VBD_(QB4) PV_(QB1)-PV_(QBn)PV_(QB2)-PV_(QBn) PV_(QB3)-PV_(QBn) PV_(QB4)-PV_(QBn)

TABLE 3 VBD tally for Running Back position in fantasy football.VBD_(RB1) VBD_(RB2) VBD_(RB3) VBD_(RB4) PV_(RB1)-PV_(RBn)PV_(RB2)-PV_(RBn) PV_(RB3)-PV_(RBn) PV_(RB4)-PV_(Rn)

The Table-2 and Table-3 each illustrates one manner in which VBD valuesmay be used, according to an embodiment. QBn and RBn may refer to aQuarterback and Running Back player, each selected for comparisonpurposes with other players of the same position. For example, anoverall point value of a tenth best Quarterback may be QBn. The rankingsof the Quarterback position may follow: QB1, QB2, QB3 . . . QBn; whilethe rankings of the Running Back position may follow: RB1, RB2, RB3 . .. RBn. The following example illustrates use of the VBD parameter. Inthe third round of a fantasy draft, a user may have a choice between thethird best Running Back (RB3) and the second best Quarterback (QB2). Ifthe VBD number between QB2, QB3 or QB4 is about the same, while there isa more drastic drop-off in the VBD value between RB2 and RB3, the usermay elect RB3 as the better draft choice, even if the point value (oralternatively the estimated points that a particular player will bringto a team) for QB2 is better.

According to an embodiment, VBD parameter 536 values may be maintainedby a draft player module that can be operated as a tool by one or moreparticipants of a fantasy league. In addition, an embodiment providesthe VBD methodology may be configured by a particular user on the-fly.For example, the user may make a new selection on who the referenceplayer is. An embodiment also provides that the values that VBDdeterminations are based on are user-configurable, with selection,omission and or weighting of certain parameters such as described inFIGS. 1-3.

Embodiments of the invention also employ the DVBD methodology tomaintain the DVBD parameter 538. As described previously, DVBDmethodology is similar to VBD methodology, but the basis of comparisonamongst players is two consecutively ranked players having the sameposition. Specifically, a tool or system may maintain DVBD values foreach player available in the draft pool. As with VBD parameter 536, theDVBD parameter 538 is intended to determine a cost associated with aparticular pick. However, as explained earlier, the DVBD differs fromthe VBD in that it compares players that are consecutively ranked at thesame position.

TABLE 4 A DVBD matrix. Position DVBD₁ DVBD₂ DVBD_(n+1) SDEV QBPV_(QB1)-PV_(QB2) PV_(QB2)-PV_(QB3) PV_(QBn+1)-PV_(QBn) SD_(QB) RBPV_(RB1)-PV_(RB2) PV_(RB2)-PV_(RB3) PV_(Rbn+1)-PV_(RBn) SD_(RB) WRPV_(WR1)-PV_(WR2) PV_(WR2)-PV_(WR3) PV_(WRn+1)-PV_(WRn) SD_(WR) TEPV_(TE1)-PV_(TE2) PV_(TE2)-PV_(TE3) PV_(TEn+1)-PV_(Ten) SD_(TE)

As shown by Table-4, the DVBD parameters may be maintained as a matrixthat is automatically updated after each draft pick. Furthermore, thevalue of the DVBD parameter 538 may be different for each user of thefantasy league, particularly when users have the option to weightparameters one way or the other. For example, if different users havedifferent weighting parameters, point values for different positions maybe different, affecting all columns in the table. An embodiment such asdescribed may provide a data structure such as illustrated by Table-4 toallow users in a fantasy league to plan and strategize draft picks. Onedraft strategy may be to select the best player at the particularposition where the Standard Deviation is the highest. This means thatthere are steeper drop-offs in terms of the point value amongst playersin the same position. As a result, the players who have the next draftpicks may lose more in terms of point value from a player at aparticular position. The net result is that the DVBD helps users devaluefor draft purposes players at the same position where there is parity.

In addition, third category 530 of draft considerations may include aparameter 540 that quantifies scheduling conflicts. The opponentschedule for each player may be evaluated to determine if too manyplayers on the same team have the same days off. In particular, if twoplayers are starters and/or have the same position, their absence in onegame may be more detrimental to a fantasy team than if the players wereabsent on different teams. In particular, NFL teams have one or twobye-weeks per season. A fantasy team may want to avoid using twostarting running backs having the same bye-week. In that case, the costof not having a starter on the bye-week may exceed the cost of losingone of two starters over two games.

Various other metrics may be tracked for purpose of determining draftselections and draft values. For example, one embodiment considers whena player will make a subsequent draft selection. In a round of 10 draftpicks, if a player has to wait 10 picks before the next pick, the metricmay be identified to the user. It is possible for certain metrics toreflect a long wait until the next round. For example, in Table-4,likely players to be selected by other users after the player's turn maybe highlighted. However, if the player has consecutive picks (such asthe 10^(th) and 11^(th) in a round of 10), no such indication may beneeded.

The various categories and parameters described in FIG. 5 may beprovided in the form of one or more modules or programs that users of afantasy league can use in order to improve their performance in afantasy league or other game simulation. For example, a module such asdescribed in FIG. 6 may be made available to users over a network.Alternatively, the module may be made available as a download or clientapplication. For example, a user may carry a draft module thatincorporates features described in FIG. 5 on a personal digitalassistant (PDA) or laptop into a live simulated draft of a fantasyleague.

FIG. 6 illustrates a method for evaluating various draft considerationsin the course of a fantasy league draft. For purpose of illustration, amethod of FIG. 6 is described in the context of the sport of football,and in particular, the NFL.

Step 610 provides that players are sorted by position. For example, thisincludes listing players by offensive position (Quarterback, RunningBack etc.), defensive position (Linebacker, Safety), and special teams(Kicker).

Step 615, the player draft is started. The order of the draft may bebased on any factor, such as randomness. Under typical fantasy leaguerules, one team is given one pick per round.

In step 620, players are ranked by position. In an embodiment, theranking is primarily based on parameters in the first category 510,although embodiments include adjusting valuations based onuser-weighting and dynamic parameters (such as opponent strength). Draftconsiderations from the third category 530 may also be considered invaluing a player.

Step 625 provides that a grouping of players of a given ranking (e.g.ranked 1-10) are compared to a common reference player. This step maycorrespond to determining the VBD parameter 536. For example, the best10 players of a particular position may be compared to the player ranked20^(th).

Step 630 provides that players are compared in position to other rankedplayers. In particular, players are compared to the next lowest playerin the ranking determined in step 620.

In step 635, a determination is made as to whether a user's pick is thatuser's first pick. If there has been a previous pick, then step 640provides that a penalty/award value is associated with each player basedon the user's previous draft pick. These may correspond to a value ofrole parameter 532 (indicating starting positions of a particular playerin the draft pool have been filled) and team parameter 534 (indicatingthat a fantasy team is overly weighed with players from one sportsteam).

If the determination in step 635 is that it is the first draft choice ofthe user, or following step 640, step 645 identifies the user's draftselection. Step 650 determines whether the pick was the last of thedraft has ended, in which case the method ends in step 655. If the draftis ongoing, step 660 provides that the next picks of the draft aremonitored until the particular user is up again in the draft. Then themethod is repeated, from step 620.

Network Architecture for Implementation of Game Simulation

A fantasy league may be implemented through numerous mediums. Animportant aspect of a fantasy league is a centralized location wherefantasy team, scores and other information is maintained. Also, it isimportant for users in the fantasy league to communicate with oneanother or at least with the service on which the fantasy league isbeing run. For this reason, fantasy leagues often involve use ofnetworks, such as the Internet.

FIG. 7 illustrates a network architecture for implementation ofembodiments described herein with a fantasy league, according to anembodiment of the invention. Embodiments of the invention include afantasy sports service that facilitates users of a fantasy sports leaguein strategy and making decisions. Components of the service 700 includea player value module 710 and a draft module 720. The player valuemodule 710 may include valuation methodologies that take into accountdynamic player value parameters, such as described in FIGS. 1-3. Theseparameters include, for example, opponent strength, player durability,and player consistency. The draft module 720 may include methodologiesdescribed in FIGS. 4-6, which facilitate draft selections for a fantasyleague user. The draft module 720 and the player value module 710 mayderive information from a database 705 or other libraries. The database705 be populated with data from various data sources 708 that maintainsports league information, including scheduling information, and playerperformance information.

Various network devices may communicate with service 700 over a datanetwork 702. Examples of network devices include desktop computers 732,laptop computers 734, smart phones 736, and wireless devices (such aspersonal digital assistants) 738. The service 700 may be suited forportable devices so that fantasy users may communicate with service 700even when the fantasy league they participate in calls for in-persondraft sessions. It is also possible for an operator/voice interface 740to provide an interface between service 700 and callers using phones 742over the voice network 704. The network devices 732-738 may communicatewith the service 700 via a graphical user-interface 730.

Alternatives for making service 700 available to users exist. In oneembodiment, a user may download components and data for service 700using a laptop or PDA and carry the service and data to a fantasy leagueparticipation forum. Other stand-alone scenarios implementations may beprovided. In addition, an embodiment may be a combined stand-alone andnetwork operated combination. For example, the user may be able toupdate data (e.g. the latest statistics or formulas) for an otherwisestand-alone application that corresponds to an embodiment describedherein. The service 700 may also be integrated into another service formwhich the fantasy league operates. For example, an Internet fantasyleague may offer service 700 as an analysis tool, at additional cost.

CONCLUSION

The foregoing description of various embodiments of the invention hasbeen presented for purposes of illustration and description. It is notintended to limit the invention to the precise forms disclosed. Manymodifications and equivalent arrangements will be apparent.

1. A computer-implemented method for facilitating user participation ina game simulation of a series of sporting events that comprise at leasta portion of an upcoming season in a professional sports league; whereinthe method comprises: using one or more processors to perform stepscomprising: determining a plurality of parameters for each of aplurality of participants of the professional sports league, whereineach participant is expected to participate in the series of sportingevents in the upcoming season of the professional sports league, whereinthe plurality of parameters include at least a first parameter that isprovided for each of the plurality of participants, wherein the firstparameter quantifies, for each participant, a strength of one or moreopponents for which that participant is scheduled to compete against inone or more of the sporting events in the upcoming season of theprofessional sports league; and indicating an overall value for each ofthe plurality of participants of the professional sports league based atleast in part on a value of each of the plurality of parameters.
 2. Themethod of claim 1, further comprising the step of detecting an inputfrom a user of the game simulation, wherein the input indicates how theplurality of parameters are to be valued in relation to one another, andwherein the step of indicating an overall value is also based at leastin part on the input.
 3. The method of claim 2, wherein the step ofdetecting an input includes detecting a weight of a value of theplurality of parameters in the indicated overall value.
 4. The method ofclaim 2, wherein indicating an overall value for one or moreparticipants of the professional sports league includes providing afirst value corresponding to an expected value of a given participant ofthe professional sports league, and providing a second valuerepresenting an adjustment to the first value, wherein the adjustment isbased at least in part on the input.
 5. The method of claim 1, whereinthe step of indicating an overall value for each of the plurality ofparticipants of the professional sports league includes determining aperformance parameter for individual participants based in part onhistorical data.
 6. The method of claim 1, wherein the plurality ofparameters include a second parameter that quantifies a durability ofone or more of the plurality of participants of the professional sportsleague based on historical data.
 7. The method of claim 1, wherein theplurality of parameters include a second parameter that quantifies aconsistency of one or more of the plurality of participants of theprofessional sports league based on historical data.
 8. The method ofclaim 1, wherein indicating an overall value for each of the pluralityof participants includes using a first input from a first user in thegame simulation to indicate a first overall value of a particularparticipant of the professional sports league to the first user for thegame simulation, and using a second input from a second user of the gamesimulation to indicate a second overall value of the particularparticipant of the professional sports league to the second user for thegame simulation.
 9. The method of claim 1, wherein the step ofindicating an overall value for each of the plurality of participants ofthe professional sports league includes basing the overall value on anaverage of at least some of the values of the plurality of parametersfor each of the plurality of participants.
 10. A computer-implementedmethod for facilitating user participation in a game simulation of aprofessional sports league, wherein the professional sports leagueincludes a plurality of games that are to be played in an upcomingseason, and wherein the method comprises: using one or more processorsto perform steps comprising: determining, for a given player in theprofessional sports league, a predictive value based on one or moreindividual games that the given player is scheduled to perform in,wherein the predictive value is determined based at least in part on apredictive analysis of how the given player will perform in the one ormore individual games of the upcoming season; and indicating, to a userof the game simulation, an overall value of the given player using thepredictive value of that player for each of the one or more individualgames of the upcoming season of the professional sports league.
 11. Themethod of claim 10, wherein the step of determining the predictive valueis based at least in part on a probabilistic determination of the givenplayer being injured in the upcoming season of the professional sportsleague.
 12. The method of claim 10, wherein the step of determining thepredictive value is based at least in part on a measurement of the givenplayer's consistency in past seasons of the professional sports league.13. The method of claim 10, wherein the step of determining thepredictive value is based on an evaluation of an opponent for the givenplayer in each of the one or more individual games of the upcomingseason of the professional sports league.
 14. The method of claim 13,wherein the evaluation is based on both a past and expected performanceof the opponent.
 15. The method of claim 13, wherein the given player isoffensive, and wherein the step of determining the predictive value isbased on an evaluation of the opponent's defense for one or more gamesof the upcoming season of the professional sports league.
 16. The methodof claim 13, wherein the given player is defensive, and wherein the stepof determining the predictive value is based on an evaluation of theopponent's offense for one or more games of the upcoming season of theprofessional sports league.
 17. The method of claim 10, wherein the stepof determining the predictive value is based on one or more of (i) aprobabilistic determination of the given player being injured in theupcoming season, (ii) a measurement of the given player's consistency inpast seasons of the professional sports league, and (iii) an evaluationof an opponent for the given player in each of the one or moreindividual games of the upcoming season of the professional sportsleague.
 18. The method of claim 10, further wherein the step ofindicating an overall value is based in-part on a past performance ofthe given player.
 19. The method of claim 10, wherein the step ofindicating an overall value includes: determining a value of a pluralityof performance parameters associated with the given player; andcombining the value of the plurality of performance parameters with thepredictive value of the given player for each of the individual games ofthe upcoming season of the professional sports league.
 20. The method ofclaim 19, wherein the step of combining the value of the plurality ofperformance parameters with the predictive value includes weighting oneor more of the predictive value and the value of each of the pluralityof performance parameters based on a weight information determined froma user of the game simulation.
 21. The method of claim 19, furthercomprising the step of defining the plurality of parameters asstatistical categories tabulated for the professional sports league of asport selected from a set consisting of American football, baseball,basketball, hockey, soccer, and auto racing.
 22. A computer-implementedmethod for evaluating players in a professional sports league forselection in a team of a fantasy league, the method comprising: usingone or more processors to perform steps comprising: designating a set ofupcoming games for which to predicatively evaluate each of a pluralityof players in the professional sports league, wherein the set ofupcoming games comprise only a portion of a season of the professionalsports league; determining a value of each player in the plurality ofplayers for the portion of the season of the professional sports league,wherein for a given player of the players, the value is based at leastin part on a performance parameter of the given player for the portionof the season as opposed to all of the season; and indicating, to a userin the fantasy league, the value of one or more of the plurality ofplayers for selection in the team of the fantasy league for a durationthat corresponds to the portion of the season of the professional sportsleague.
 23. The method of claim 22, wherein the performance parametercorresponds to a predictive estimation of the given player's durabilityfor playing in at least a portion of the set of upcoming games.
 24. Themethod of claim 22, wherein the performance parameter corresponds to apredictive estimation of the given player's consistency in participatingin at least a portion of the set of upcoming games.
 25. The method ofclaim 22, wherein the performance parameter corresponds to an estimatestrength of schedule for the given player's team in at least a portionof the set of upcoming games.
 26. A system for facilitating userparticipation in a game simulation of a professional sports league,wherein the game simulation is conducted by a plurality of users over anetwork, and wherein the system comprises: one or more processors thatexecute instructions to implement: a player valuation module configuredto: associate a plurality of parameters to each of a plurality ofplayers in the sports league, wherein the plurality of parametersinclude at least a predictive first parameter that is provided for eachplayer of the professional sports league, wherein the first parameterquantifies, for each player, a strength of one or more opponents forwhich that player is scheduled to compete against in one or moreupcoming games in a season of the professional sports league; determinevalues for the plurality of parameters for each of the plurality ofplayers in the professional sports league based at least in part onhistorical data; process an input from a user on how the plurality ofparameters are to be valued in relation to one another; indicate for theuser an overall value of a given player in the professional sportsleague based on the values for the plurality of parameters for the givenplayer and the input; and an interface generated for the system for usewith the player valuation module in order to enable the user to enterthe input.
 27. The system of claim 26, wherein the interface isconfigured to present the overall value of the given player of theprofessional sports league to the user.
 28. The system of claim 26,wherein the player valuation module is programmed to access and retrieveat least some of the historical data from one or more libraries over thenetwork.
 29. A computer-implemented method for facilitating analysis ofa game simulation of a series of sporting events that comprises aportion of an upcoming season in a professional sports league orassociation, wherein the method comprises: using one or more processorsto perform steps comprising: determining a plurality of parameters foreach of a plurality of participants that are to partake in the series ofsporting events that comprise the portion of the upcoming season in theprofessional sports league, wherein the plurality of parameters includeat least a performance-based parameter and a non-performance-basedparameter; detecting a user-specific input from a user of the gamesimulation, wherein the input indicates how the plurality of parameters,including the non-performance-based parameter, are to be valued inrelation to one another; and indicating an overall value for one or moreparticipants of the plurality of participants that partake in theprofessional sports league who are designated for inclusion in the gamesimulation based on the plurality of parameters and the user-specificinput.
 30. The method of claim 29, wherein the step of detecting auser-specific input includes detecting a weight of a value of theplurality of parameters in the indicated overall value.
 31. The methodof claim 29, wherein the step of indicating an overall value includesproviding a single value for each participant of the professional sportsleague corresponding to the indicated overall value.
 32. The method ofclaim 29, wherein the step of indicating an overall value includesdisplaying multiple values that correspond to or based at least in parton the plurality of parameters.
 33. The method of claim 29, wherein thestep of indicating an overall value for one or more participants thatpartake in the professional sports league includes determining aperformance parameter for individual participants in the plurality ofparticipants based in part on historical data.
 34. The method of claim33, wherein the step of determining the performance parameter includesdetermining a predictive value that measures a participant's durabilityfrom performance of that participant in past seasons of the professionalsports league.
 35. The method of claim 33, wherein the step ofdetermining the performance parameter includes determining a predictivevalue that measures a participant's consistency from performance of thatparticipant in past seasons of the professional sports league.
 36. Themethod of claim 29, wherein indicating an overall value for one or moreparticipants that partake in the professional sports league includesproviding a first expected value of a given participant, and providing asecond value representing an adjustment to the first expected value,wherein the adjustment is based at least in part on the input.
 37. Themethod of claim 29, wherein indicating an overall value for one or moreparticipants that partake in the professional sports league includesusing a first input from a first user to indicate a first overall valueof a particular participant to the first user, and using a second inputfrom a second user to indicate a second overall value of the particularparticipant to the second user.
 38. A computer-implemented method forevaluating players of a spectator sports league during a player draft ina fantasy league, wherein the fantasy league is operated for a pluralityof users who form fantasy teams based on picks during the player draft,the method comprising: using one or more processors to perform stepscomprising: determining an overall value of a player of the spectatorsports league based in part on a predictive evaluation of the player,using in part, historical data for that player; for a given user of thefantasy league, after one or more draft picks, indicating an adjustmentto the overall value of the player in relation to other players in thespectator sports league based on another player that the given user hasalready selected for the given user's fantasy team during the playerdraft.
 39. The method of claim 38, wherein the step of indicating anadjustment to the overall value of the player includes tracking eachplayer in the sports league that the given user selects with a draftpick.
 40. The method of claim 38, wherein the step of indicating anadjustment to the overall value of the player includes indicating areduction of the overall value for a first player in relation to otherplayers in the sports league based on the given user having previouslyselected a second player having a position of the first player.
 41. Themethod of claim 38, wherein the step of indicating an adjustment to theoverall value of the player includes indicating a reduction of theoverall value for a first player in relation to other players in thesports league based on the given user having previously selected asecond player that is on a common team in the sports league with thefirst player.
 42. The method of claim 38, wherein the step of indicatingan adjustment to the overall value of the player includes indicating areduction of the overall value for a first player in relation to otherplayers in the sports league based on the given user having previouslyselected a second player that has a scheduling conflict with the firstplayer.
 43. The method of claim 42, wherein the first player and thesecond player are in a professional American football league, andwherein the scheduling conflict corresponds to a team of the firstplayer and a team of the second player each having a same bye week. 44.The method of claim 38, wherein the step of indicating an adjustment tothe overall value of the player includes indicating a draft value inaddition to the overall value as two or more separate metrics.
 45. Themethod of claim 38, wherein the step of indicating the adjustmentincludes considering when the given user of the fantasy league will makeanother player selection based on an order of the player draft.
 46. Themethod of claim 38, further comprising the step of receiving an inputfrom the given user of the fantasy league that specifies a weighting ofone or more parameters for determining at least one of the overall valueand indicating the adjustment.
 47. A computer-implemented method forevaluating players of a spectator sports league during a player draft ina fantasy league, the method being implemented through use of one ormore processors, the method comprising: using the one or more processorsto perform steps comprising: determining a value of each player of aplurality of players in the spectator sports league for use in thefantasy league, wherein the value for each player is based at least inpart on a predictive evaluation of the player that includesconsideration of a plurality of parameters for an upcoming season of thespectator sports league; maintaining a comparison of the value of eachplayer in a set of players, wherein the set of players includesindividuals who play in the spectator sports league at one of aplurality of positions, and wherein the comparison indicates adifference in the value of each player in the set to a reference value;detecting a user-specific input from a user of the game simulation,wherein the input indicates how the plurality of parameters are to bevalued in relation to one another; updating, after one or more picks ofthe player draft, the value of a player of the players for a given userbased on an adjustment that accounts for another player that the givenuser of the fantasy league has already selected during the player draft;and in response to updating the value, displaying the adjusted value ofthe player.
 48. The method of claim 47, wherein the reference for thecomparison is based on a designated player in the sports league whoplays one of the plurality of positions.
 49. The method of claim 47,further comprising the steps of: tracking individual draft picks by theusers of the fantasy league; and after each individual draft pick,updating at least the comparison of the value of each player in the setof players that is available after the given draft pick.
 50. The methodof claim 49, further comprising the step of making a ranking ofavailable players in the set of players, wherein each available playeris available for selection by a subsequent draft pick, and wherein theranking is based at least in part on the value of that player asupdated.
 51. The method of claim 50, further comprising the step ofupdating the ranking of the players after a draft pick.
 52. The methodof claim 49, wherein for a given user in the fantasy league, the step ofdetermining the value for a given player in the plurality of playersfactors in one or more of (i) another player in the sports league thatthe given user selected with a previous draft pick playing a position ofthe given player, (ii) another player in the sports league that thegiven user selected with a previous draft pick playing on a team in thesports league of the given player, or (iii) another player in the sportsleague that the given user selected with a previous draft pick playingon a team in the sports league that has a scheduling conflict with ateam of the given player.
 53. The method of claim 52, wherein the stepof determining the value for a given player in the plurality of playersfactors in a number of draft picks between a current upcoming draft pickof the given user and a next draft pick of the given user.
 54. Themethod of claim 47, wherein the comparison indicates the difference inthe value of each player in the set of players to a common referenceplayer who plays in the sports league at the plurality of positions. 55.The method of claim 47, wherein the step of maintaining a comparison ofthe value of each player includes selecting, for one or more availableplayers in the set of players after the one or more draft pick, anotheravailable player in the set of players based on the other availableplayer's ranking after that draft pick, wherein the reference value isthe value of the selected other available player.
 56. The method ofclaim 55, wherein the step of selecting another available playerincludes selecting the player in the set of players who has a nextimmediate ranking position after that draft pick.
 57. The method ofclaim 47, wherein at least one of the value and the comparison ismaintained separately for one or more users in the fantasy league basedon an action by the one or more users.